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Feature separation and adversarial training for the patient-independent detection of epileptic seizures

Overview of attention for article published in Frontiers in Computational Neuroscience, July 2023
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  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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2 X users

Citations

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2 Dimensions

Readers on

mendeley
2 Mendeley
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Title
Feature separation and adversarial training for the patient-independent detection of epileptic seizures
Published in
Frontiers in Computational Neuroscience, July 2023
DOI 10.3389/fncom.2023.1195334
Pubmed ID
Authors

Yong Yang, Feng Li, Xiaolin Qin, Han Wen, Xiaoguang Lin, Dong Huang

Timeline

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X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 1 50%
Professor 1 50%
Readers by discipline Count As %
Unspecified 1 50%
Psychology 1 50%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 August 2023.
All research outputs
#20,750,401
of 26,411,386 outputs
Outputs from Frontiers in Computational Neuroscience
#1,036
of 1,503 outputs
Outputs of similar age
#261,978
of 375,010 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#10
of 28 outputs
Altmetric has tracked 26,411,386 research outputs across all sources so far. This one is in the 18th percentile – i.e., 18% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,503 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.0. This one is in the 24th percentile – i.e., 24% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 375,010 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 28 others from the same source and published within six weeks on either side of this one. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.